Predicting Tree Growth and Transpiration in Forests: An Analysis of a Small-Scale Dataset With Pareto Optimized Tsaug Augmentation

Abstract

The study demonstrates the potential of specifically developed data augmentation in estimating tree growth and transpiration by emphasizing the influence of environmental variables, such as photosynthetically active radiation (PAR), air temperature, and relative humidity—on tree growth predictions. The investigation utilizes data obtained from two hemi-boreal semi-natural mixed conifer deciduous forest sites in the Aukstaitija National Park in Lithuania. Field measurements included xylem sap flow measurements and stem circumference increment growth. The dataset utilized in the analysis consisted of four trees per species and contained information on tree growth, transpiration, and solar angle measurements. Pareto-optimized Tsaug augmentation techniques were employed to diversify the dataset, generating augmented time series to improve diversity and minimize distortion. The results of the correlation analysis indicated significant relationships between environmental variables and tree growth and transpiration. The Prophet based prediction model, notably when trained with augmented data, outperformed other models in predicting tree growth and perspiration variables (MAPE ranging from 0.0017 to 0.01). This was particularly evident for FACP, FAGP, and FADP variables, showcasing substantial improvement with augmented data.